Several short and long-term phenomena appearing in liquid treatment processes influences dosing of liquid treatment chemicals. There are often large variations in the quantity of liquid to be treated. For example the consumption of industrial or drinking water and the amount of waste water greatly vary depending on the plant capacity, time, season, weather, etc. The quality of the incoming water, e.g. turbidity, suspended solids, pH, phosphorus, temperature, also varies rapidly sometimes.
Today, chemicals are often dosed on the basis of incoming flow rate in liquid treatment plants. This does not guarantee the treatment efficiency, if the quality of liquid greatly changes. This leads easily to over or under dose of chemicals depending on the quality of liquid. Many complex phenomena, such as coagulation and flocculation take place in liquid treatments. The effect of chemicals on treatment result is non-linear.
Feedforward dosing on the basis of incoming flow rate does not ensure sufficient treatment results since the quality of the liquid is changing. Feedback control with a PID (Proportional, Integral and Derivative) controller provides improvement if fluctuations are fairly small. Variations in operating conditions require adaptation of the controllers. A gradual adaptation of the PID parameters can be done by gain scheduling but it is limited to a fixed control structure. Flexible control structure can be realized by switching strategies. However, both gain scheduling and switching strategies require complex logic for adaptation in multivariable systems. Also smooth and reliable operation is difficult to achieve. The PID parameters can also be tuned on-line, e.g. by self-tuning PID controllers.
One example of adaptively designing self-tuning PID controller is disclosed in WO0198845 and it can be characterized by parameter values derived from interpolation of process model parameters. Parameters characterize each of the models. A value of the parameters is selected from a set of predetermined initialization values. For each parameter value so-called accumulated Norm (which is derived from a model square error calculated for the models) is calculated as repeated evaluations of models are conducted. For each parameter an adaptive parameter value (a weighted average of the initialization values) is also calculated. The set of adaptive process parameter value are then used to redesign a process controller. Parameterized controller is applied to adaptive feedforward/feedback PID control.
Self-tuning PID controllers are efficient in adapting only to slow changes in operating conditions, as such adaptation mechanism is fairly slow. Disturbances during the adaptation may cause serious problems. Limitations become more prominent in multivariable systems.
Adaptive fuzzy control is less sensitive to disturbances than self-tuning PID controller. One example of multi-region fuzzy logic control system is disclosed in U.S. Pat. No. 6,041,320. The system utilizes an auxiliary process variable to determine which of several regions of different gain a non-linear process is operating. The operation area of a process is divided into different regions in advance. Each region has its own controller which is implemented by a linear set of membership functions and rules. The auxiliary variables in each rules is used for selection of proper region. Solution becomes impractical with increasing number of regions and auxiliary process variables as each region has its own controller. This leads large number of parameters and difficulties to generalize the control system to new applications.
All these methodologies lead to extremely complicated control structures containing multiple interactive and dependent parameters which are difficult to handle. The adaptation logic becomes complicated as the number of parameters handled increases. The operation of different controllers can be presented with nearly similar control surfaces in certain process conditions. However, it is essential how the adaptation mechanism is done. Compact adaptation structure is necessary in changing operating conditions and implementing controllers into new processes. The present inventors have published a conference paper concerning general state of the art (Esko Juuso, Katja Virret and Marjatta Piironen “Intelligent methods in dosing control of water treatment”, in: Proceedings of Workshop on Applications in Chemical and Biochemical Industry, ERUDIT, Sep. 15, 1999). Paper describes a preliminary modeling experiment for water treatment by linguistic equation (LE) method but not LE-based control in water treatment. In the paper it is mentioned that LE-controller can be adaptive like any other controller.
The current invention is founded on a novel approach where dosing control is based on compact control system with adaptive and non-linear features. Smooth and fast adaptation to rapid variations improves the performance of dosing control. Adaptive LE controllers can operate at large range of process conditions because the adaptation is based on detection of the process state in advance. Predefined adaptation approaches addressed by the inventive method for dose control does not require time consuming identification of models or parameters. Compact parameter set is also beneficial in the tuning of the LE controllers. In the present invention the number of parameters is small compared to the current adaptive controllers known in the art, and all the parameters are understandable giving insight on the process operation.